# Source code for botorch.posteriors.deterministic

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# LICENSE file in the root directory of this source tree.

r"""
Deterministic (degenerate) posteriors. Used in conjunction with deterministic
models.
"""

from __future__ import annotations

from typing import Optional
from warnings import warn

import torch
from botorch.posteriors.posterior import Posterior
from torch import Tensor

[docs]class DeterministicPosterior(Posterior):
r"""Deterministic posterior.

[DEPRECATED] Use EnsemblePosterior instead.
"""

def __init__(self, values: Tensor) -> None:
r"""
Args:
values: Values of the samples produced by this posterior.
"""
warn(
"DeterministicPosterior is marked for deprecation, consider using "
"EnsemblePosterior.",
DeprecationWarning,
)
self.values = values

@property
def device(self) -> torch.device:
r"""The torch device of the posterior."""
return self.values.device

@property
def dtype(self) -> torch.dtype:
r"""The torch dtype of the posterior."""
return self.values.dtype

def _extended_shape(
self, sample_shape: torch.Size = torch.Size()  # noqa: B008
) -> torch.Size:
r"""Returns the shape of the samples produced by the posterior with
the given sample_shape.
"""
return sample_shape + self.values.shape

@property
def mean(self) -> Tensor:
r"""The mean of the posterior as a (b) x n x m-dim Tensor."""
return self.values

@property
def variance(self) -> Tensor:
r"""The variance of the posterior as a (b) x n x m-dim Tensor.

As this is a deterministic posterior, this is a tensor of zeros.
"""

[docs]    def rsample(
self,
sample_shape: Optional[torch.Size] = None,
) -> Tensor:
r"""Sample from the posterior (with gradients).

For the deterministic posterior, this just returns the values expanded
to the requested shape.

Args:
sample_shape: A torch.Size object specifying the sample shape. To
draw n samples, set to torch.Size([n]). To draw b batches
of n samples each, set to torch.Size([b, n]).

Returns:
Samples from the posterior, a tensor of shape
self._extended_shape(sample_shape=sample_shape).
"""
if sample_shape is None:
sample_shape = torch.Size([1])
return self.values.expand(self._extended_shape(sample_shape))